近20 a环渤海地区GS_NDVI时空分异及其对气候变化和LUCC的响应

doi:10.16258/j.cnki.1674-5906.2021.12.001

生态环境学报 ›› 2021, Vol. 30 ›› Issue (12): 2275-2284.DOI: 10.16258/j.cnki.1674-5906.2021.12.001

• 研究论文 • 下一篇

近20 a环渤海地区GS_NDVI时空分异及其对气候变化和LUCC的响应

黄栋¹^,²(), 李鹏¹^,², 董南³^,^*()

1.教育部人文社科重点研究基地/海洋经济与可持续发展研究中心,辽宁大连 116029
2.辽宁师范大学海洋可持续发展研究院,辽宁大连 116029
3.建设综合勘查研究设计院有限公司,北京 100007

收稿日期:2021-03-19 出版日期:2021-12-18 发布日期:2022-01-04
通讯作者: *董南（1984年生）,男,副研究员,注册测绘师,博士,主要从事智慧城市、遥感与GIS应用研究。E-mail: dongnan67@126.com
作者简介:黄栋（1989年生）,女,讲师,博士,研究方向为资源环境数据分析与应用。E-mail: imdon@126.com
基金资助:
辽宁省社会科学规划基金办公室青年项目(L20CJY004);辽宁省教育厅青年项目(LQ2020030)

Spatial-temporal Differentiation of GS_NDVI in Recent 20 Years and Its Responses to Climate Change and LUCC in the Bohai Coastal Region

HUANG Dong¹^,²(), LI Peng¹^,², DONG Nan³^,^*()

1. Key Research Base of Humanities and Social Sciences of the Ministry of Education/Center for Studies of Marine Economy and Sustainable Development, Dalian 116029, China
2. Institute of Marine Sustainable Development, Liaoning Normal University, Dalian 116029, China
3. Comprehensive Institute of Geotechnical Investigation and Surveying, Company Limited, Beijing 100007, China

Received:2021-03-19 Online:2021-12-18 Published:2022-01-04

摘要/Abstract

摘要：

植被是全球或区域生态环境变化监测的“指示器”,利用NDVI（Normal Difference Vegetation Index,归一化植被指数）作为表征植被生长状况的指示因子,监测其动态变化并探讨其与相关因素间的关系,对区域生态环境可持续发展具有重要意义。选取2000—2018年环渤海地区SPOT/VEGETATION NDVI数据集,采用相关分析、稳定性评价和变化趋势分析等方法,从区域和像元尺度获取GS_NDVI（Growing Season NDVI,植被生长季NDVI）动态变化和空间分异及其对气候和LUCC的响应。得到以下主要结论：从区域尺度来看,近20 a以来环渤海地区植被覆盖状况有较为明显的改善,区域平均GS_NDVI由0.6267提升至0.6755;从像元尺度来看,62.23%的像元GS_NDVI呈改善趋势,24.63%的像元NDVI呈退化趋势,环渤海“C”型带北部城市改善趋势最为明显且稳定性较高;华北地区（天津市、唐山市）、临海区和城镇扩张区域植被退化严重且稳定性差,并且越邻近岸线,GS_NDVI随时间变化的稳定性越差;气候因素对GS_NDVI变化的影响十分有限（显著相关的像元数量不超过10%）,其中GS_NDVI变化与降水的相关关系相较于气温更为密切;区域土地利用基本格局决定了环渤海地区GS_NDVI的空间格局,建设用地的扩张是导致区域植被退化的关键因素;人类活动驱动下土地利用变化是主导GS_NDVI退化以及不稳定变化的关键因素。

关键词: 植被指数, 时空分异, 气候变化, LUCC, 环渤海地区

Abstract:

Vegetation change is an important indicator of ecological environment monitoring. Using NDVI (Normal Difference Vegetation Index) to monitor the dynamics of vegetation change and study its influencing factors is of great significance to the sustainable development of the ecological environment. We select the SPOT/VEGETATION NDVI dataset of the Bohai coastal from 2000 to 2018, and adopted correlation analysis, stability evaluation, trend analysis to analyze the dynamic changes of regional vegetation and its responses to climate changes and LUCC at the pixel and regional scales. The results show that: (1) On the regional scale, the vegetation coverage of the Bohai coastal region improved significantly from 2000 to 2018, along with the average value of GS_NDVI (Growing Season NDVI) increased from 0.6267 to 0.6755. (2) On the pixel scale, the number of pixels showing an improvement trend accounted for 62.23%, and 24.62% of the pixels showed degradation trend. Furthermore, the northern cities of the study area showed a more obvious improvement trend and higher stability. Central cities (mainly including Tianjin and Tangshan), inshore and urban expansion regions have obvious degradation trends and poor stability. Moreover, the nearer the distance to the coast line, the weaker of the stability was. (3) The influences of climate on the GS_NDVI variation were limited (the number of pixels with significant correlation was less than 10%), and the correlation between precipitation and GS_NDVI was much closer than temperature. (4) The spatial pattern of GS_NDVI in the study area was mainly determined by the land use pattern. Urban expansion was a key factor for vegetation degradation, and LUCC driven by human activities was the main factor leading to the degradation and unstable changes of GS_NDVI.

Key words: GS_NDVI, spatial-temporal differentiation, climate change, LUCC, Bohai coastal region

中图分类号:

Q948
X171.1

黄栋, 李鹏, 董南. 近20 a环渤海地区GS_NDVI时空分异及其对气候变化和LUCC的响应[J]. 生态环境学报, 2021, 30(12): 2275-2284.

HUANG Dong, LI Peng, DONG Nan. Spatial-temporal Differentiation of GS_NDVI in Recent 20 Years and Its Responses to Climate Change and LUCC in the Bohai Coastal Region[J]. Ecology and Environment, 2021, 30(12): 2275-2284.

图/表 16

参考文献 31

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数据名称 Name of the data	数据格式 Data type	数据时相 Data time	空间分辨率与比例尺 Resolution and Scale	数据来源 Data sources
市行政边界 Boundary of the cities	矢量 Vector	2015	1:100000	RESDC
土地利用数据 Land use	栅格 Grid	2000/2010/2018	1 km	RESDC
DEM	栅格 Grid	2000	30m	RESDC
气象站点 Meteorological stations	矢量 Vector	2010	—	CMDSC
降水、气温数据 Precipitation and temperature data	文本 Text	2000—2018	—	CMDSC
中国季度植被指数空间分布数据 Spatial distribution dataset of quarterly NDVI of China	栅格 Grid	2000—2018	1 km	RESDC

数据名称 Name of the data	数据格式 Data type	数据时相 Data time	空间分辨率与比例尺 Resolution and Scale	数据来源 Data sources
市行政边界 Boundary of the cities	矢量 Vector	2015	1:100000	RESDC
土地利用数据 Land use	栅格 Grid	2000/2010/2018	1 km	RESDC
DEM	栅格 Grid	2000	30m	RESDC
气象站点 Meteorological stations	矢量 Vector	2010	—	CMDSC
降水、气温数据 Precipitation and temperature data	文本 Text	2000—2018	—	CMDSC
中国季度植被指数空间分布数据 Spatial distribution dataset of quarterly NDVI of China	栅格 Grid	2000—2018	1 km	RESDC

变异系数 C_variation	分类 Classification	面积占比/% Area ratio/%
C_variation<0.10	弱变异 Weak variation	82.04
0.10<C_variation<0.50	中等变异 Medium variation	15.07
0.50<C_variation<1.00	较强变异 Strong variation	1.98
C_variation>1.00	强变异 Very strong variation	0.91

变异系数 C_variation	分类 Classification	面积占比/% Area ratio/%
C_variation<0.10	弱变异 Weak variation	82.04
0.10<C_variation<0.50	中等变异 Medium variation	15.07
0.50<C_variation<1.00	较强变异 Strong variation	1.98
C_variation>1.00	强变异 Very strong variation	0.91

变化趋势斜率 Slope of the changing trend	变化程度 Changing degree	面积占比 Area ratio/%
<-0.010	明显退化 Obvious degradation	3.97
-0.010— -0.005	中度退化 Medium degradation	6.96
-0.005— -0.001	轻度退化 Light degradation	13.70
-0.001—0.001	基本不变 Unchanged	13.13
0.001—0.005	轻度改善 Light improvement	36.23
0.005—0.010	中度改善 Medium improvement	23.28
>0.010	明显改善 Obvious improvement	2.71

近20 a环渤海地区GS_NDVI时空分异及其对气候变化和LUCC的响应

Spatial-temporal Differentiation of GS_NDVI in Recent 20 Years and Its Responses to Climate Change and LUCC in the Bohai Coastal Region

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参考文献 31

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土地利用类型 Land use type	2000年 Year 2000		2010年 Year 2010		2018年 Year 2018
土地利用类型 Land use type	面积 Area/ km²	面积占比 Area ratio/ %	面积 Area/ km²	面积占比 Area ratio/ %	面积 Area/ km²	面积占比 Area ratio/ %
耕地 Cropland	77237	57.01	76638	56.57	73565	54.30
林地 Woodland	20679	15.26	20608	15.21	20652	15.24
草地 Grassland	9902	7.31	9170	6.77	6851	5.06
水域 Water body	6915	5.10	7005	5.17	11128	8.21
建设用地 Built-up land	16076	11.87	18368	13.56	21214	15.66
未利用地 Unused land	3327	2.46	2743	2.02	2073	1.53

	土地利用类型 Land use type	2018年 Year 2018
	土地利用类型 Land use type	耕地 Cropland	林地 Woodland	草地 Grassland	水域 Water body	建设用地 Built-up land	未利用地 Unused land	总计 Total
2000年 Year 2000	耕地 Cropland	59360	4550	2143	2630	10723	305	79712
	林地 Woodland	4467	13069	1432	395	1099	20	20483
	草地 Grassland	3123	2234	2486	752	765	218	9579
	水域 Water body	1911	325	249	2530	935	465	6415
	建设用地 Built-up land	5933	434	279	2811	4349	112	13919
	未利用地 Unused land	1092	26	49	834	299	873	3173
	总计 Total	75887	20639	6637	9953	18172	1993	133280

GS_NDVI变化程度 Changing degree of GS_NDVI	土地利用变化（2000—2018年） Land use change (Year 2000 to 2018)	占该类像元比例 Proportion of pixels in corresponding class/%
明显退化 Obvious degradation	耕地→建设用地 Cropland→Built-up land	26.94
明显退化 Obvious degradation	耕地→耕地 Cropland→Cropland	24.12
中度退化 Medium degradation	耕地→耕地 Cropland→Cropland	33.13
中度退化 Medium degradation	耕地→建设用地 Cropland→built-up land	19.43
轻度退化 Light degradation	耕地→耕地 Cropland→Cropland	42.64
轻度退化 Light degradation	耕地→建设用地 Cropland→built-up land	12.20
基本不变 Unchanged	耕地→耕地 Cropland→Cropland	45.78
基本不变 Unchanged	耕地→建设用地 Cropland→Built-up land	9.03
轻度改善 Light improvement	耕地→耕地 Cropland→Cropland	46.41
轻度改善 Light improvement	林地→林地 Woodland→Woodland	9.02
中度改善 Medium improvement	耕地→耕地 Cropland→Cropland	35.07
中度改善 Medium improvement	林地→林地 Woodland→Woodland	21.84
明显改善 Obvious improvement	耕地→耕地 Cropland→Cropland	36.53
明显改善 Obvious improvement	建设用地→建设用地 Built-up land→Built-up land	6.56

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